Early detection is essential to minimize morbidity and mortality in the event of a bioterrorist attack. Development of surveillance systems to detect early symptoms of possible bioterrorism (BT) agents has been driven by the urgency of implementing "real-time" programs following the terrorism attacks in 2001. Syndromic surveillance systems (SSS), based on collection of illness syndromes rather than clinical or laboratory confirmed diseases, largely depend on manual or semi-automated transfer of data from emergency departments to public health authorities. Systems that evaluate ambulatory care visits have relied on broadly defined discharge ICD-9 code groups to define syndromes. Very few of the current systems incorporate patient-specific clinical data, from electronic medical records, such as are available in the Veterans Health Administration (VHA). SSS based on ICD-9 codes alone is unlikely to demonstrate sufficient sensitivity and specificity to serve as a useful real-time system for identifying and responding to potential BT events. However, detection systems that incorporate clinical and laboratory variables may significantly improve operating characteristics. Data mining from a computerized patient record system (CPRS) can be automated and may provide a robust, cost-effective BT SSS. We propose that modeled syndromes based on ICD-9 codes in combination with selected CPRS parameters are superior to modeled syndromes based on ICD-9 code-only for the early detection of BT. Our objectives are to develop and validate a CPRS-based prediction rule based on combinations of electronically available clinical and laboratory data to accurately track disease syndromes most consistent with the characteristics of a BT event and to implement a scalable automated SSS that can serve as a model for similar development in other areas of VHA. This project will address this in the following specific aims: 1) Compare the sensitivity and specificity of using ICD-9 codes only vs ICD-9 plus CPRS clinical parameters for detecting influenza-like illnesses, a BT syndrome surrogate, 2) Evaluate the validity of the SSS by applying the new syndrome definitions to prospective, real-time VHA data and comparing the results with active influenza surveillance data collected through other community based systems, 3) Evaluate the SSS ability to detect BT outbreaks by introducing simulated datasets into the system, and 4) Identify barriers and requirements to expand the SSS from VISN 5 to VISN 19.